Most real world systems (such as national/global businesses, financial/stock markets, energy management systems, biological systems, pharmaceutical industries, internet traffic control, industrial control systems, etc.) are becoming complex with large numbers of inputs whose interactions, relations and effects on the system output are quite complex and are often unable to be fully understood and analysed by application users. In addition, recent advances in technology have resulted in the ability to easily acquire and store large amounts of numerical and linguistic data thus requiring the ability to handle quantitative and qualitative data sets of a given specific system. This has resulted in the need to provide novel systems that can handle large amounts of vague or complex data and make sense of them to accurately model the system and improve its performance. There is a need also to accurately identify important factors affecting the outputs as well as determining dependencies and relationships between the given system inputs and outputs in a uniquely intuitive and understandable way and thus creating accurate models (in an understandable linguistic format) of the system to be controlled. The developed controllers can then be employed to produce accurate predictions for the system outputs given the system inputs and justifying in an easy to understand user friendly human language why a given output was achieved. There is a need also for optimisation methods which can find the optimal system inputs to achieve a desired control output. There is a still further need for methods which allow group decision making to integrate data across various experts, locations, departments, sectors and systems to provide a best practice/benchmarking model and having decisions similar to those extracted from a panel of experts.
In financial stock market applications, there is a need to identify and understand from a huge number of factors, what primary factors will affect the stock prices for a given bank or company, and how they would effect an increase or decrease in the stock price. There is a need to provide such a model in an easy to understand linguistic model to enable the system to be transparent and fully understood to the user. For example the stock prices of a leading high street bank such as HSBC would be affected by specific primary factors that would determine how the price would move. Factors affecting the stock price could include: the stocks of related companies in the same sector, general economic data from countries the bank trades in, trading statements, competitor data, fixed and variable data, and other historical information. From this vast amount of data there is a need for a way of identifying the key primary factors affecting the stock price, show the relationships between these primary factors to determine how they affect the stock in order to accurately predict the future movement of the stock. Furthermore, there would be the need to provide efficient controllers that could predict the stock price given the current state. There is a need also to know what are the values of the controller inputs that will help to achieve a given increase in the stock prices (say 10%). In addition to data, the opinions and preference of human market commentators could also be of considerable value in taking into consideration the market trends, and trader opinions on the predicted movement of the stock price.
In the area of energy management. There is a need to identify the relations between the various factors (such as inside/outside temperature, activity, cloud cover, wind speed, number of occupants, etc) and energy consumption to create an accurate model of the system. This model could be used afterwards to develop a controller that can suggest optimised energy set points that will help to reduce the energy consumption in homes and offices. There is a need also to provide a method that will be able to indentify the optimum specific values of the given set points that will allow for example to achieve a 10% in energy savings while providing linguistic easy to understand justifications for the given decision which provide the user with a totally transparent system. The system will also be able to integrate linguistic advice from human experts on how to reduce energy.
The proposed system could also be employed in biological systems where better understanding is needed of which specific gene(s) in the enormous human genome or other factors are responsible for the prevalence of specific kind of disease and the importance/weights of the these given factors. Also rather than dealing with statistics which can be complicated to understand and analyse, the system can provide a linguistic and easy to understand representation that can indicate that if the given factors are high, low, etc, the possibility of the given disease cancer would be low. The system can also give a linguistic advice on how to reduce the possibility of the occurrence of a given disease. The system can also provide a best practice that can integrate information from various countries/experts to provide a best practice to reduce the possibility of the occurrence of a given disease and what are the preventative measures we can take to avoid this disease.
In another potential application, an automotive dealer business comprises of several hundred input factors such as part sales, number of repair bays and demographic data affecting its profits and operational costs in different areas. Here there is a need to accurately identify which key business indicators directly affect specific parts of the business and how, so that managers can make more informed decisions by predicting how these key indicators will cause an increase in profitability, decrease costs, etc. The system advises the user also on what the optimum values of the input parameters should be to achieve a given output (given increase in profit, given efficiency improvement, given cost reduction, etc.) while providing linguistic easy to understand justifications for the given decision which provide the user with a totally transparent system. The system also integrates the expert data from the various dealers (say 600 dealers as in the case of Peugeot) to provide a best practice (consensus modelling) for all the different dealers across the country.
Another possible application for our system is in local authorities and social welfare services such as The Integrated Children's System (ICS), which keep a huge amount of consistent data on child welfare case histories. Due to the vast amount of numerical and linguistic characteristics affecting the care of each child, it would be difficult for end users to identify trends and key risk factors that could lead to child abuse and neglect. For example in an inquiry into the death of a baby, it emerged that step fathers and frequent changes of social workers were factors that the investigation highlighted as high risks for potentially leading to cases of child abuse future. Currently there are no commercial systems which can analyse huge amounts of historical information on such case histories to identify these key potential risk factors. The ability for a system to both identify and show how these factors would be attributed to cases of child abuse, in a clear understandable way, would help social workers take early preventative action to safe guard children in care. In addition best practice amongst the social welfare experts across the country could be identified by taking into account opinions and recommendations of social works, to help improve the quality of service.
Another possible application is in the areas of Human Resources (HR) and recruitment sectors. In these sectors, the system aims to automatically characterise application or domain specific group decision models that can be used to classify, score and rank data and information specific to that domain. A decision is defined by a set of attributes that are either known or observed to best characterise that decision. Human experts and data sources can be used to elicit and extract domain specific characteristics pertaining to different domain and application decisions.
In many organisations, particularly those having a large number of members, certain decisions regarding the organisation require input from more than one individual. Each of these individuals, depending on experience, specialised knowledge base, as well as their personality will provide a different view on the decision. The consistency of these individuals also varies depending on the level of knowledge and expertise.
It remains therefore a problem to incorporate these views while determining the reliability of the information, into the final decision making methodology. One typical solution is to organise a meeting between the individuals at which it is hoped a consensus can be reached. Alternatively one person within the organisation may be tasked with making the final decision based on the input. In other applications where there are multiple different sources of information it is a problem to effectively analyse and model the data to account for the vagueness and uncertainties in the information and to incorporate these varied models into the final decision making methodology and derive effective and correct recommendations from the information to help end users make more informed decisions.
The above mentioned characteristics have not been achieved by the existing commercial systems. The invention therefore seeks to address the above deficiencies and provide an improved decision making process.
Firstly the most influential set of input features that affect the system outputs as well as the relative weighting of the different influential input features need to be identified. In addition, there is a need to provide in an easy to understand linguistic format the relationship between the system inputs and outputs. Various feature selection methods have been proposed to address the selection of the most relevant features for a classification task. In Cardie, “Proceedings of the Tenth International Conference on Machine Learning, 25-32”, (1993)” and John et al. “Proceedings of International Conference on Machine Learning 121-9, (1994)”. decision trees have been applied to find relevant features by keeping only those that appear in the decision tree. Principle Component Analysis is used to reduce complex data with a large number of attributes into lower dimensions to determine subtle features within the data. These approaches however do not provide a means showing the degree of influence and affect each input feature has on the output.
Feature weighting is an approach that seeks to estimate the relative importance of each feature (with respect to the classification task), and assign it a corresponding weight (Xinchuan Proceedings of the IEEE Joint Conference on Neural Networks, Vol 2, 1327-30, (2004)). It is suitable for tasks in which the relevance of the attributes needs to be determined. Several examples of feature weighting approaches can be found in the literature. Neural Networks can be used as a method for feature weighting where by the importance of a feature is extracted based on the strengths (weights) of related links in a trained neural network. Hence, Neural Networks unlike other feature selection methods not only extract the important and relevant input features, but Neural Networks can also identify the degree of influence and affect each input feature has on the output (i.e. the weight importance of the given important input features). In addition, Neural Networks have many advantages over other feature weighting mechanism as Neural Networks are characterised by being able to learn and adapt from training noisy data and they are capable of acting as universal appoximators. In addition neural networks, once trained, can provide a fast mapping from inputs to outputs. Neural networks therefore have the potential to better capture the most relevant features related to a classification task. However, Neural Networks suffer from the problem of being seen as a black box where it does not produce its learnt weights in a user friendly format that can be understood by the system user.
Another major problem addressed by the invention is the ability of the methods to generate a system model that can be easily read and analysed by the human user. Fuzzy Logic Systems (FLSs) have been used with great success to develop universal approximators that can handle the uncertainties and model the system performance using an easy to understand linguistic labels (such as Low, Medium, . . . ) and IF-Then rules. FLSs provide a framework for designing robust systems that are able to deliver a satisfactory performance when contending with the uncertainty, noise and imprecision attributed to real world applications. FLSs also allow information to be represented in a transparent and flexible human readable form.
However, there is a need to develop learning mechanisms that can learn and adapt the fuzzy systems parameters to the changing environments and system conditions. In addition, for applications to which the invention can be applied, there is an advantage for the learning mechanism to be online, fast and one pass learning method rather than an iterative learning method.
When dealing with the process of human decision making in real world applications the classification and aggregation of knowledge and information leads to uncertainties due to different opinions and preferences of experts, the varying sources of information and the vagueness and imprecision in the data. It is therefore impossible to quantify group decision-making using traditional mathematical models. For example the task of formulating a new person specification (job requirement) for a given job role is the responsibility of the organisation's Human Resources (HR) manager. This usually involves a group decision-making process to derive a collective opinion from a selection panel of individuals who have expertise related to the occupation domain associated with the job role.
Each expert's opinions and preferences for the job requirements can vary based on their roles in the organisation, knowledge and experience pertaining to the occupation domain. Each expert can also consider certain characteristics more or less important than others. The variations in the opinions of experts cause high level of uncertainties when specifying the job requirements. Each expert's opinions and preferences for the job requirements can vary based on their roles in the organisation, knowledge and experience pertaining to the occupation domain.
Each expert can also consider certain characteristics more or less important than others and it is not always clear without observing the expert's decision making behaviour which characteristics most influence a ranking decision. Due to the varying knowledge and experiences of different experts, not all experts will be consistent in their opinions and in applying their preferences for consistently ranking different applicants with similar abilities in the same way. It is therefore important to identify and give a higher weighting to the opinions of more reliable and experienced experts over those who are less consistent in their decision making behaviour. The variations in the opinions and consistencies of different experts cause high degrees of uncertainties. Conventional attempts at addressing these uncertainties are through meetings and discussion sessions, which can be both time consuming and difficult to coordinate for different departments and divisions of the organisation. The varying opinions of the experts can make it difficult to achieve an agreement or consensus among the group. In addition, the final decision may not always reflect the opinions of all the experts in an objective way.
The difficulty increases for big multinational organisations where distributed experts need to collaborate to develop an international advert for a given job role. Thus the process of human decision making is naturally uncertain due to the inherent subjectivity and vagueness in the articulation and aggregation of human opinions and preferences. Due to the unsuitability of mathematical models to handle these sources of uncertainty and due the need to employ human understandable systems, FLSs have been used in the area of group decision making.
There are several approaches within the literature that use fuzzy logic for modelling group decision making process. These models deal with decision situations in which a set of experts have to choose the best alternative or alternatives from a feasible set of alternatives. The different processes which have been focused on are: the consensus process and selection process Alonso et al. Proceedings of the IEEE International Conference on Fuzzy Systems, 1818-23, (2007). The former consists of obtaining the highest consensus (agreement) among experts to obtain a state where the opinions of the different experts are as close as possible to one another. The latter process consists of obtaining the final solution to the problem from the opinions expressed by the experts in the consensus process. Recent work presented an automated system that handles incomplete and imprecise knowledge about experts' preferences using incomplete fuzzy preference relations. The consensus producing mechanism is an iterative process with several consensus rounds, in which the experts accept to change their preferences following advice generated by the system in order to obtain a solution with a high degree of consensus between the experts. In these systems there is also much focus throughout the process on maintaining consistency of information and avoiding contradiction between the opinions and preferences of different experts.
The approaches outlined above are based on type-1 fuzzy logic approaches for achieving a group consensus on a set of known solutions. However, these type-1 approaches do not aim to model and handle the high levels of uncertainties involved within the group decision process.
Type-2 fuzzy systems could be used to handle the uncertainties in the group decision making process as they can model the uncertainties between expert preferences using type-2 fuzzy sets. A type-2 fuzzy set is characterized by a fuzzy Membership Function (MF), i.e. the membership value (or membership grade) for each element of this set is a fuzzy set in [0,1], unlike a type-1 fuzzy set where the membership grade is a crisp number in [0,1]. The MFs of type-2 fuzzy sets are three dimensional and include a Footprint Of Uncertainty (FOU). Hence, type-2 fuzzy sets provide additional degrees of freedom that can make it possible to model the group uncertainties between the varying opinions and preferences of experts.
Current existing and commercial systems mostly rely on mathematical/statistical modelling or expert systems. Traditional expert systems are static models which do not reflect real data and provide acceptable results or explanations for the aforementioned domains. The current mathematical modelling techniques lack visibility and transparency because they cannot be easily understood and analysed by the end user. There is therefore a need to produce intelligent systems that can provide in an easy to interpret linguistic format, a more intuitive way of identifying the relations and interactions between the various inputs and the system outputs.
Most predictive analysis systems are simplistic in the way in which they only predict how specific outcomes such as production costs are affected by certain changes in key business indicators such as labour and parts. They do not however provide a means of accurately determining what exact optimum increases or reductions in these parameters will be needed to achieve a 30% reduction in production costs. There is therefore a need also to supply an intelligent decision support mechanism that is able to advise the user on how to maximise performance, improve efficiency or reduce costs by a desired amount, giving accurate and exact explanations of how they can achieve this and justify their decisions.
The predictive models generated from other commercial systems are not flexible enough to handle vague and uncertain information that exist in real world applications, and are unable to adapt to changing circumstances.
Even if a system includes neural networks or fuzzy systems, the system cannot process data and produce an interpretation of the system operation as well as advising on what will be the optimum system input parameters to realise a given output value and handle the uncertainties involved with the group decision processes.
Finally there is a need for a system that can integrate various different data sources and suggestions related to different sites and human expert recommendations together within a single framework. This can provide a unique value in being able to identity best practices across divisions or branches within large organisations and determine benchmarks for improving efficiency and profitability.
In the following subsection, background material is presented concerning some of the computational intelligence concepts employed throughout the invention description.
The invention comprises novel computational intelligence mechanisms and controllers that can analyse large amounts of vague or complex numerical and linguistic data and thus allowing the to ability to handle quantitative and qualitative data to accurately identify important factors affecting the outputs as well as determining dependencies and relationships between the given system inputs and outputs in a uniquely intuitive and understandable way and thus creating accurate model (in an understandable linguistic format) of the system to be controlled. The invented controllers can then produce accurate predictions for the system outputs given the system inputs and justifying in an easy to understand user friendly human language why a given output was achieved. The invention has also optimisation methods which can find the optimal system inputs to achieve a desired control output. In addition, the invention has methods which allows group decision making which is based on type-2 fuzzy systems to integrate data across various experts, dealers and locations to provide a best practice/benchmarking model and having decisions similar to those extracted from a panel of experts.